Identification of Alzheimer’s Disease Using Novel Dual Decomposition Technique and Machine Learning Algorithms from EEG Signals

Abstract

Alzheimer's disease (AD) is one of the neurodegenerative disorders. The rate of AD prevalence is rapidly increasing worldwide. The existing clinical invasive methods and neuro-imaging techniques to detect AD are time-consuming, subjective, and expensive. To overcome these issues, we proposed a new automatic framework for detecting AD at an early stage based on the dual decomposition method. Initially, EEG signals of mild cognitive impairment (MCI), AD, and normal control (NC) patients are divided into five subbands by employing discrete wavelet transform (DWT). Subsequently, a Variational mode decomposition (VMD) is applied to these five EEG subbands for further decomposition into various intrinsic mode functions (IMFs). Afterward, three different multiscale permutation entropy (PE) features, namely Shannon PE (SPE), Tsalli's PE (TPE), and Renyi PE (RPE), have been measured from each IMF. Later, these features have been used to train and test ensemble bagged tree (EBT), k-nearest neighbor, support vector machine (SVM), decision tree (DT), and neural networks with a 10-fold cross-validation scheme. The proposed method has been verified using EEG signals of 59-AD, 7-MCI, and 102-NC subjects. The results obtained from the proposed DWT-VMD method provide 95.20% accuracy for three-class and 97.70% for two-class classification using an EBT classifier with 10-fold cross-validation. It shows a significant ability to distinguish AD from MCI. The proposed dual decomposition method can employ for other neurodegenerative disorders such as Parkinson's disease, epilepsy, various sleep disorders, and major depressive disorders.

Is EEG Entropy a Useful Measure for Alzheimer's Disease?

“…A total of 40 articles (Table 3 , Ref. [ 20 , 25 , 26 , 27 , 28 , 30 , 31 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ]) published between 2000 and 2023 using entropy measures were included (Fig. 2 ).…”

“…Machine learning-based approaches have been successfully applied to discriminate AD patients from HC, MCI subjects, and individuals with Subjective Cognitive Complaint (SCC) (Table 4 , Ref. [ 20 , 25 , 26 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 58 , 59 , 61 , 62 , 63 , 64 ]). These studies have also found significantly lower entropy values in AD than in HC and a high discriminative capability [ 20 , 25 , 36 , 37 , 40 , 58 , 59 , 62 , 63 ].…”

“…[ 20 , 25 , 26 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 58 , 59 , 61 , 62 , 63 , 64 ]). These studies have also found significantly lower entropy values in AD than in HC and a high discriminative capability [ 20 , 25 , 36 , 37 , 40 , 58 , 59 , 62 , 63 ]. Some studies revealed that MCI patients have intermediate entropy values between AD patients and those in HC [ 37 , 60 ].…”

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

“…Learning from historical data, artificial neural networks (ANNs) may produce intricate patterns and improve prediction accuracy (Cepowski et al, 2021;Ihsan et al, 2023). Since they can generalize from trained data, they are perfect for scenarios involving large, diverse datasets and enable better operational planning, prediction, and management for a large number of fields such as education (Haque et al, 2024;Yaqin et al, 2021), society and human (Capote-Leiva et al, 2022;Kannan et al, 2023), energy and fuels (Le et al, 2023;Notton et al, 2019;Taghavifar and Perera, 2023), economy (Moonlight et al, 2023;Suvon et al, 2023), medicine (Puri et al, 2023;Yeo et al, 2023), manufacturing and industry (Cong My et al, 2023;Radonjić et al, 2020;Rosiani et al, 2023), environment (Biswas et al, 2024;Zarra et al, 2019), food and agriculture (Chaivivatrakul et al, 2022;Swasono et al, 2022), transportation (Abramowski, 2008;H. P. Nguyen et al, 2024a;V.…”